SOURCE-LINKED INTELLIGENCE
ESTS at WMT26: Routing-Informed Expert Pruning for Model Compression
We describe six submissions under the team name ESTS to the unconstrained WMT26 Model Compression Shared Task for English--Simplified Chinese and English--Egyptian Arabic. We submit three compression operating points per translation direction, all derived from GPT-OSS-20B. We use task-specific routing mass to rank experts and cross-lingual routing divergence to allocate retained capacity across layers, then physically remove low-importance experts. The resulting specialists are recovery-tuned on GPT-5.1-generated synthetic translation data and further compressed by applying MXFP4 quantization
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-11T00:30:43.000Z
First collected: 2026-09-20T18:42:18.733Z. This is not the publication date.